Impact Damage Detection and Identification Using Eddy Current Pulsed Thermography Through Integration of PCA and ICA
Impact Damage Detection and Identification Using Eddy Current Pulsed Thermography Through Integration of PCA and ICA
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DOI：
10.1109/jsen.2014.2301168
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发表时间：
2014-05-01
影响因子：
4.3
通讯作者：
Berthiau, Gerard
中科院分区：
文献类型：
--
作者：
Cheng, Liang;Gao, Bin;Berthiau, Gerard
采用涡流脉冲热成像（ECPT）技术对碳纤维增强塑料（CFRP）试样的冲击损伤和由此产生的损伤进行检测和分离。纤维纹理的复杂性和非均匀性以及多缺陷的检测、识别和表征。本文提出了一种结合主成分分析（PCA）和独立成分分析（ICA）的瞬态热视频分析方法。该方法无需任何训练知识，即可根据瞬态响应行为提取时空模式。第一步，利用主成分分析将数据变换到正交主成分子空间并进行降维；然后采用多通道形态分量分析方法，利用多通道形态分量分析的稀疏性和独立性，检测和分离CFRP中不同层、缺陷及其组合信息的影响。由于存在不同的瞬态行为，通过计算冲击ECPT序列与无缺陷ECPT序列估计混合向量的相互关系，可以识别和分离多种类型的缺陷。
Eddy current pulsed thermography (ECPT) is implemented for detection and separation of impact damage and resulting damages in carbon fiber reinforced plastic (CFRP) samples. Complexity and nonhomogeneity of fiber texture as well as multiple defects limit detection identification and characterization from transient images of the ECPT. In this paper, an integration of principal component analysis (PCA) and independent component analysis (ICA) on transient thermal videos has been proposed. This method enables spatial and temporal patterns to be extracted according to the transient response behavior without any training knowledge. In the first step, using the PCA, the data is transformed to orthogonal principal component subspace and the dimension is reduced. Multichannel morphological component analysis, as an ICA method, is then implemented to deal with the sparse and independence property for detecting and separating the influences of different layers, defects, and their combination information in the CFRP. Because different transient behaviors exist, multiple types of defects can be identified and separated by calculating the cross-correlation of the estimated mixing vectors between impact the ECPT sequences and nondefect ECPT sequences.